When investigating a remotely located object, multiple imaging and analysis tools may be used to determine the nature of the object. For example, cameras may capture a two dimensional (2D) image of the object. Images acquired from cameras are beneficial in that they provide high resolution color data of whatever object/scene is being imaged. However, 2D images from a camera do not provide any kind of ranging information, meaning that it is hard to use a single image from a camera to generate a 3D representation of the object.
Ranging systems are devices that measure distances to remote objects, enabling the creation of a cloud of 3D coordinates that represent the surface of the object. However, ranging systems lack color data, and are often much lower resolution than images taken by a camera. Therefore, a cloud of 3D coordinates for a ranging system is hard to interpret unless it is “backfilled” with additional coordinates. During the backfill process, coordinates are added to the cloud, and the position of each added coordinate is extrapolated based on the positions of neighboring coordinates in the cloud.
Backfill techniques remain imperfect and processing intensive. Users therefore continue to desire backfill techniques that operate quickly and efficiently to enhance the resolution of 3D coordinate clouds (also known as “point clouds”).